Bus arrival time prediction at bus stop with multiple routes

Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes.     

Hybrid model for prediction of bus arrival times at next station

Effective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time‐of‐day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering‐based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)–based methods. Copyright © 2010 John Wiley & Sons, Ltd.     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

A probabilistic model for traffic at actuated control signals

Vehicle actuated controls are designed to adapt green and red times automatically, according to the actual dynamics of the arrival, departure and queuing processes. In turn, drivers experience variable delays and waiting times at these signals. However, in practice, delays and waiting times are computed at these systems with models that assume stationariety in the arrival process, and that are capable of computing simply expectation values, while no information is given on the uncertainty around this expectation. The growing interest on measures like travel time reliability, or network robustness motivates the development of models able to quantify the variability of traffic at these systems.This paper presents a new modeling approach for estimating queues and signal phase times, based on probabilistic theory. This model overcomes the limitations of existing models in that it does not assume stationary arrival rates, but it assumes any temporal distribution as input, and allows one to compute the temporal evolution of queue length and signal sequence probabilities. By doing so, one can also quantify the uncertainty in the estimation of delays and waiting times as time-dependent processes. The results of the probabilistic approach have been compared to the results of repeated microscopic simulations, showing good agreement. The smaller number of parameters and shorter computing times required in the probabilistic approach makes the model suitable for, e.g., planning and design problems, as well as model-based travel time estimation.     

Bus arrival time calculation model based on smart card data

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.     

Modelling the capacity of closely-spaced parallel runways using innovative approach procedures

This paper develops analytical models for calculating the ultimate arrival, departure, and mixed operation capacity of closely-spaced parallel runways. Each capacity is defined as the maximum number of corresponding aircraft operations accommodated during a given period of time (usually one or a quarter of an hour) under constant (i.e. sustained) demand for service. As combined, they enable the capacity coverage curve to be synthesized. In particular, the capacity model for arrivals assumes the use of two rather innovative approach procedures – the Staggered Approach Procedure (SGAP) and the Steeper Approach Procedure (SEAP) in combination with the baseline Conventional Approach Procedure (CNAP) under Instrument Meteorological Conditions (IMC) and Instrumental Flight Rules (IFR).The model for arrival capacity that aims to estimate potential of these procedures uses main inputs such as: the geometry of given parallel runways and innovative SGAP and SEAP, and baseline CNAP; the Air Traffic Control (ATC) minimum separation rules; aircraft fleet mix characterized by the wake-vortex categories; capability of using different ILS Glide (GS) angles; final approach speeds; and the arrival runway occupancy time. In addition, the model for departure capacity uses inputs that embrace: the ATC separation rules between successive departures; aircraft fleet mix; and corresponding runway occupancy times. Finally, the model for mixed operation capacity uses inputs such as: the ATC arrival–departure separation rules; corresponding runway occupancy times; aircraft fleet mix for each type of operations; and proportion of the arrival and departure demand.The models are applied to calculating the ultimate capacity of closely-spaced parallel runways at two large airports, one in Europe and other in the US, assuming safe use of innovative SGAP and SEAP in combination with CNAP under IMC. The output from the models consists of the corresponding capacities and their variations depending on particular inputs.     

Multi-regime arrival rate uniform delay models for signalized intersections

This paper presents the development and validation of uniform delay models for coordinated signalized intersections. The Highway Capacity Manual (HCM) identifies one uniform and five non-uniform (platoon) arrival types. Delays for non-uniform arrival cases are computed by applying progression adjustment factors (PFs) to the delay for uniform arrival case. The range of PF is from 0% to 256%. We found that the PF approach produced accurate results for only one-half of cases. This paper presents an Arrival-Based approach that eliminates the needs for applying PF. The AB approach directly considers the effects of quality of progression in formulating delay models. It uses different flow rates for vehicles within and outside platoons. A total of 11 different delay models were derived to cover all arrival cases. Data from three different states were used for validation of AB delay models. The results indicate that AB models provided accurate results for all arrival types. However, HCM uniform delay model was not accurate for Arrival Types 1, 4 and 6. Furthermore, the results of cycle-by-cycle delay analyses showed that the difference between field delays and AB models were not significant, but that was not the case for the HCM model. The AB models can be simplified to yield the HCM uniform delay model, if a single regime arrival rate is assumed. Single regime arrival rate implies that the flow rate for vehicles in platoon is the same as those arriving randomly. For only the uniform arrival case, the AB delay model is identical to the HCM delay model; thus making the HCM uniform delay model a special case of AB models.     

Improved modeling of park‐and‐ride transfer time: Capturing the within‐day dynamics

An important factor that affects park‐and‐ride demand is transfer time. However, conventional park‐and‐ride demand models treat transfer time as a single value, without considering the time‐of‐day effect. Since early comers usually occupy spots closer to the entrance, their transfer times are shorter. Hence, there is a relationship between arrival time and transfer time. To analyze this relationship, a micro‐simulation model is developed. The model simulates the queuing system at the entrance and the pattern that parking spots are occupied in the parking lot over time. As expected, the model output illustrates an increasing relationship between arrival time and transfer time. This relationship has significant implication in mode choice models because it means that the attractiveness of park‐and‐ride depends on the time of arrival at the park‐and‐ride lot. This model of park‐and‐ride transfer time can potentially improve travel demand forecasting, as well as facilitate the operation and design of park‐and‐ride facilities.     

Development of a probabilistic model to optimize disseminated real‐time bus arrival information for pre‐trip passengers

In the advent of Advanced Traveler Information Systems (ATIS), the total wait time of passengers for buses may be reduced by disseminating real‐time bus arrival times for the next or series of buses to pre‐trip passengers through various media (e.g., internet, mobile phones, and personal digital assistants). A probabilistic model is desirable and developed in this study, while realistic distributions of bus and passenger arrivals are considered. The disseminated bus arrival time is optimized by minimizing the total wait time incurred by pre‐trip passengers, and its impact to the total wait time under both late and early bus arrival conditions is studied. Relations between the optimal disseminated bus arrival time and major model parameters, such as the mean and standard deviation of arrival times for buses and pre‐trip passengers, are investigated. Analytical results are presented based on Normal and Lognormal distributions of bus arrivals and Gumbel distribution of pre‐trip passenger arrivals at a designated stop. The developed methodology can be practically applied to any arrival distributions of buses and passengers.     

Dynamic models of commuter behavior: Experimental investigation and application to the analysis of planned traffic disruptions

This paper reviews the results of a series of experiments aimed at investigating the day-to-day dynamics of commuter behavior in congested traffic systems. The interactive experiments involve actual work commuters in a simulated traffic system, whereby commuters noncooperatively supply their decisions to a traffic simulation model that determines the resulting arrival times and associated trip times; these in turn form the basis of the commuters' decisions on the next day. Models are developed to predict the daily switching of departure time and/or route by individual commuters in response to experienced congestion in the system or to exogenously supplied information. These models are incorporated in a dynamic modelling framework for the analysis of the impacts of planned traffic disruptions, such as those associated with major highway repair and reconstruction activities.     

基于灰色神经网络的输油管道运行费用预测

输油管道运行费用的预测在原油运输中有着重要意义,文中将灰色预测模型与神经网络预测模型结合起来,建立灰色神经网络预测模型,对输油管道运行费用进行预测。灰色神经网络预测模型充分发挥了灰色预测模型和神经网络预测模型样本少、计算速度快的优点。计算结果表明：灰色神经网络与EBP神经网络相比,预测模型精度高,计算量小,收敛速度快。     

Optimal shipment decisions for an airfreight forwarder: Formulation and solution methods

We study the freight forwarder’s shipment planning problem in an airfreight forwarding network where a set of cargo shipments have to be transported to given destinations. We provide mixed integer programming formulations that use piecewise-linear cargo rates and account for volume and weight constraints, flight departure/arrival times, as well as shipment-ready times.After exploring the solution of such models using CPLEX, we devise two solution methodologies to handle large problem sizes. The first is based on Lagrangian relaxation, where the problems decompose into a set of knapsack problems and a set of network flow problems. The second is a local branching heuristic that combines branching ideas and local search. The two approaches show promising results in providing good quality heuristic solutions within reasonable computational times, for difficult and large shipment consolidation problems.     

Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis

Waiting time at public transport stops is perceived by passengers to be more onerous than in-vehicle time, hence it strongly influences the attractiveness and use of public transport. Transport models traditionally assume that average waiting times are half the service headway by assuming random passenger arrivals. However, research agree that two distinct passenger behaviour types exist: one group arrives randomly, whereas another group actively tries to minimise their waiting time by arriving in a timely manner at the scheduled departure time. This study proposes a general framework for estimating passenger waiting times which incorporates the arrival patterns of these two groups explicitly, namely by using a mixture distribution consisting of a uniform and a beta distribution. The framework is empirically validated using a large-scale automatic fare collection system from the Greater Copenhagen Area covering metro, suburban, and regional rail stations thereby giving a range of service headways from 2 to 60 min. It was shown that the proposed mixture distribution is superior to other distributions proposed in the literature. This can improve waiting time estimations in public transport models. The results show that even at 5-min headways 43% of passengers arrive in a timely manner to stations when timetables are available. The results bear important policy implications in terms of providing actual timetables, even at high service frequencies, in order for passengers to be able to minimise their waiting times.     

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

Abstract

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.     

Controlled time of arrival windows for already initiated energy-neutral continuous descent operations

Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control is used to achieve different metering times). Based on previous works, an optimal control problem is formulated and numerically solved. The earliest and latest times of arrival at the initial approach fix have been computed for the descent of an Airbus A320 under different scenarios, considering the potential altitudes and distances to go when receiving the controlled time of arrival update. The effects of the aircraft mass, initial speed, longitudinal wind and position of the initial approach fix on the time window have been also investigated. Results show that time windows about three minutes could be achieved for certain conditions, and that there is a trade-off between robustness facing controlled time of arrival updates during the descent and fuel consumption. Interestingly, minimum fuel trajectories almost correspond to those of minimum time.     

Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: Nonlinear integer programming models with linear constraints

This paper focuses on how to minimize the total passenger waiting time at stations by computing and adjusting train timetables for a rail corridor with given time-varying origin-to-destination passenger demand matrices. Given predetermined train skip-stop patterns, a unified quadratic integer programming model with linear constraints is developed to jointly synchronize effective passenger loading time windows and train arrival and departure times at each station. A set of quadratic and quasi-quadratic objective functions are proposed to precisely formulate the total waiting time under both minute-dependent demand and hour-dependent demand volumes from different origin–destination pairs. We construct mathematically rigorous and algorithmically tractable nonlinear mixed integer programming models for both real-time scheduling and medium-term planning applications. The proposed models are implemented using general purpose high-level optimization solvers, and the model effectiveness is further examined through numerical experiments of real-world rail train timetabling test cases.     

Optimizing coordinated transfer with probabilistic vehicle arrivals and passengers' walking time

Supporting efficient connections by synchronizing vehicle arrival time and passengers' walking time at a transfer hub may significantly improve service quality, stimulate demand, and increase productivity. However, vehicle travel times and walking times in urban settings often varies spatially and temporally due to a variety of factors. Nevertheless, the reservation of slack time and/or the justification of vehicle arrival time at the hub may substantially increase the success of transfer coordination. To this end, this paper develops a model that considers probabilistic vehicle arrivals and passengers walking speeds so that the slack time and the scheduled bus arrival time can be optimized by minimizing the total system cost. A case study is conducted in which the developed model is applied to optimize the coordination of multiple bus routes connecting at a transfer station in Xi'an, China. The relationship between decision variables and model parameters, including the mean and the standard deviation of walking time, is explored. It was found that the joint impact of probabilistic vehicle arrivals and passengers' walking time significantly affects the efficiency of coordinated transfer. The established methodology can essentially be applied to any distribution of bus arrival and passenger walking time. Copyright © 2017 John Wiley & Sons, Ltd.     

基于小波变换和模态声发射的管道中导波传播特性的试验研究

基于小波变换和模态声发射理论,通过模态声发射试验的方法确定了管道中导波传播的频散特性。通过分析声发射信号的Gabor小波变换幅度在时频空间分布特点,确定某一频率下某一模态导波到达传感器的时间,从而确定该频率下该模态导波的群速度,进而确定其频散曲线。试验确定管道中频散曲线与理论计算曲线较好吻合,证明小波变换是分析频散波时频特性的有效工具。     

Charge timing choice behavior of battery electric vehicle users

This paper aims to examine choice behavior in respect of the time at which battery electric vehicle users charge their vehicles. The focus is on normal charging after the last trip of the day, and the alternatives presented are no charging, charging immediately after arrival, nighttime charging, and charging at other times. A mixed logit model with unobserved heterogeneity is applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Estimation results, obtained using separate models for commercial and private vehicles, suggest that state of charge, interval in days before the next travel day, and vehicle-kilometers to be traveled on the next travel day are the main predictors for whether a user charges the vehicle or not, that the experience of fast charging negatively affects normal charging, and that users tend to charge during the nighttime in the latter half of the trial. On the other hand, the probability of normal charging after the last trip of a working day is increased for commercial vehicles, while is decreased for private vehicles. Commercial vehicles tend not to be charged when they arrival during the nighttime, while private vehicles tend to be charged immediately. Further, the correlations of nighttime charging with charging immediately and charging at other times reveal that it may be possible to encourage charging during off-peak hours to lessen the load on the electricity grid. This finding is supported by the high variance for the alternative of nighttime charging.     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.